Added cross referencing more advanced architectures implementations

README.md

@@ -6,6 +6,8 @@ license: mit
 
 This repository provides a detailed guide and implementation of the Transformer architecture from the ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762) paper. The implementation focuses on understanding each component through clear code, comprehensive testing, and visual aids.
 
+For implementions of more recent architectural innovations from DeepSeek, see the **Related Implementations** section.
+
 ## Table of Contents
 1. [Summary and Key Insights](#summary-and-key-insights)
 2. [Implementation Details](#implementation-details)
@@ -213,3 +215,15 @@ The implementation includes visualizations of:
 These visualizations help understand the inner workings of the transformer and verify correct implementation.
 
 For detailed code and interactive examples, please refer to the complete implementation notebook.
+
+## Related Implementations
+
+This repository is part of a series implementing the key architectural innovations from the DeepSeek paper:
+
+1. **[Transformer Implementation Tutorial](https://huggingface.co/datasets/bird-of-paradise/transformer-from-scratch-tutorial)**(This Tutorial): A detailed tutorial on implementing transformer architecture with explanations of key components.
+
+2. **[DeepSeek Multi-head Latent Attention](https://huggingface.co/bird-of-paradise/deepseek-mla)**: Implementation of DeepSeek's MLA mechanism for efficient KV cache usage during inference.
+
+3. **[DeepSeek MoE](https://huggingface.co/bird-of-paradise/deepseek-moe)**: Implementation of DeepSeek's Mixture of Experts architecture that enables efficient scaling of model parameters.
+
+Together, these implementations cover the core innovations that power DeepSeek's state-of-the-art performance. By combining the MoE architecture with Multi-head Latent Attention, you can build a complete DeepSeek-style model with improved training efficiency and inference performance.